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<div class="title">cpc_segmentation.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2014-, Open Perception, Inc.</span></div>
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<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160; </div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#ifndef PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#define PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#include &lt;pcl/segmentation/cpc_segmentation.h&gt;</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160; </div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt;</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<a class="code" href="classpcl_1_1_c_p_c_segmentation.html">pcl::CPCSegmentation&lt;PointT&gt;::CPCSegmentation</a> () :</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    max_cuts_ (20),</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    min_segment_size_for_cutting_ (400),</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    min_cut_score_ (0.16),</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    use_local_constrains_ (true),</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    use_directed_weights_ (true),</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    ransac_itrs_ (10000)</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;{</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;}</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160; </div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt;</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<a class="code" href="classpcl_1_1_c_p_c_segmentation.html">pcl::CPCSegmentation&lt;PointT&gt;::~CPCSegmentation</a> ()</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;{</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;}</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160; </div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">   60</a></span>&#160;<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">pcl::CPCSegmentation&lt;PointT&gt;::segment</a> ()</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;{</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="keywordflow">if</span> (supervoxels_set_)</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  {</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <span class="comment">// Calculate for every Edge if the connection is convex or invalid</span></div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  <span class="comment">// This effectively performs the segmentation.</span></div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    calculateConvexConnections (sv_adjacency_list_);</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160; </div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="comment">// Correct edge relations using extended convexity definition if k&gt;0</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    applyKconvexity (k_factor_);</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160; </div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    <span class="comment">// Determine wether to use cutting planes</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    doGrouping ();</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    grouping_data_valid_ = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    applyCuttingPlane (max_cuts_);</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    </div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="comment">// merge small segments</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    mergeSmallSegments ();</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  }</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;[pcl::CPCSegmentation::segment] WARNING: Call function setInputSupervoxels first. Nothing has been done. \n&quot;</span>);</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;}</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">   86</a></span>&#160;<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">pcl::CPCSegmentation&lt;PointT&gt;::applyCuttingPlane</a> (uint32_t depth_levels_left)</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;{</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <span class="keyword">typedef</span> std::map&lt;uint32_t, pcl::PointCloud&lt;WeightSACPointType&gt;::Ptr&gt; SegLabel2ClusterMap;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  pcl::console::print_info (<span class="stringliteral">&quot;Cutting at level %d (maximum %d)\n&quot;</span>, max_cuts_ - depth_levels_left + 1, max_cuts_);</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  <span class="comment">// stop if we reached the 0 level</span></div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  <span class="keywordflow">if</span> (depth_levels_left &lt;= 0)</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  SegLabel2ClusterMap seg_to_edge_points_map;</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  std::map&lt;uint32_t, std::vector&lt;EdgeID&gt; &gt; seg_to_edgeIDs_map;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  EdgeIterator edge_itr, edge_itr_end, next_edge;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  boost::tie (edge_itr, edge_itr_end) = boost::edges (sv_adjacency_list_);</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  <span class="keywordflow">for</span> (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    next_edge++;  <span class="comment">// next_edge iterator is neccessary, because removing an edge invalidates the iterator to the current edge</span></div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    uint32_t source_sv_label = sv_adjacency_list_[boost::source (*edge_itr, sv_adjacency_list_)];</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    uint32_t target_sv_label = sv_adjacency_list_[boost::target (*edge_itr, sv_adjacency_list_)];</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160; </div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    uint32_t source_segment_label = sv_label_to_seg_label_map_[source_sv_label];</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    uint32_t target_segment_label = sv_label_to_seg_label_map_[target_sv_label];</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="comment">// do not process edges which already split two segments</span></div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <span class="keywordflow">if</span> (source_segment_label != target_segment_label)</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="comment">// if edge has been used for cutting already do not use it again</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="keywordflow">if</span> (sv_adjacency_list_[*edge_itr].used_for_cutting)</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="comment">// get centroids of vertices</span></div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> source_centroid = sv_label_to_supervoxel_map_[source_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> target_centroid = sv_label_to_supervoxel_map_[target_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160; </div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="comment">// stores the information about the edge cloud (used for the weighted ransac)</span></div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="comment">// we use the normal to express the direction of the connection</span></div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="comment">// we use the intensity to express the normal differences between supervoxel patches. &lt;=0: Convex, &gt;0: Concave</span></div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <a class="code" href="structpcl_1_1_point_x_y_z_i_normal.html">WeightSACPointType</a> edge_centroid;</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    edge_centroid.getVector3fMap () = (source_centroid.getVector3fMap () + target_centroid.getVector3fMap ()) / 2;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160; </div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    <span class="comment">// we use the normal to express the direction of the connection!</span></div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    edge_centroid.getNormalVector3fMap () = (target_centroid.getVector3fMap () - source_centroid.getVector3fMap ()).normalized ();</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="comment">// we use the intensity to express the normal differences between supervoxel patches. &lt;=0: Convex, &gt;0: Concave</span></div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    edge_centroid.intensity = sv_adjacency_list_[*edge_itr].is_convex ? -sv_adjacency_list_[*edge_itr].normal_difference : sv_adjacency_list_[*edge_itr].normal_difference;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    <span class="keywordflow">if</span> (seg_to_edge_points_map.find (source_segment_label) == seg_to_edge_points_map.end ())</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    {</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      seg_to_edge_points_map[source_segment_label] = pcl::PointCloud&lt;WeightSACPointType&gt;::Ptr (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;WeightSACPointType&gt;</a> ());</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    }</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    seg_to_edge_points_map[source_segment_label]-&gt;push_back (edge_centroid);</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    seg_to_edgeIDs_map[source_segment_label].push_back (*edge_itr);</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;  }</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  <span class="keywordtype">bool</span> cut_found = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  <span class="comment">// do the following processing for each segment separately</span></div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="keywordflow">for</span> (SegLabel2ClusterMap::iterator itr = seg_to_edge_points_map.begin (); itr != seg_to_edge_points_map.end (); ++itr)</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  {</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="comment">// if too small do not process</span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="keywordflow">if</span> (itr-&gt;second-&gt;size () &lt; min_segment_size_for_cutting_)</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    {</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    }</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    std::vector&lt;double&gt; weights;</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    weights.resize (itr-&gt;second-&gt;size ());</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="keywordflow">for</span> (std::size_t cp = 0; cp &lt; itr-&gt;second-&gt;size (); ++cp)</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    {</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      <span class="keywordtype">float</span>&amp; cur_weight = itr-&gt;second-&gt;points[cp].intensity;</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      cur_weight = cur_weight &lt; concavity_tolerance_threshold_ ? 0 : 1;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      weights[cp] = cur_weight;</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    }</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160; </div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    pcl::PointCloud&lt;WeightSACPointType&gt;::Ptr edge_cloud_cluster  = itr-&gt;second;</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    pcl::SampleConsensusModelPlane&lt;WeightSACPointType&gt;::Ptr model_p (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_sample_consensus_model_plane.html">pcl::SampleConsensusModelPlane&lt;WeightSACPointType&gt;</a> (edge_cloud_cluster));</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160; </div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html">WeightedRandomSampleConsensus</a> weight_sac (model_p, seed_resolution_, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    weight_sac.<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a6313e39510545b961cfbed0373b7bbde">setWeights</a> (weights, use_directed_weights_);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    weight_sac.<a class="code" href="classpcl_1_1_sample_consensus.html#af8558bc2462b6da4a2f88b2efc1ad571">setMaxIterations</a> (ransac_itrs_);</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    <span class="comment">// if not enough inliers are found</span></div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <span class="keywordflow">if</span> (!weight_sac.<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aaa2dc352bd71e275a23de67ac522974f">computeModel</a> ())</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    {</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    }</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160; </div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    Eigen::VectorXf model_coefficients;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    weight_sac.<a class="code" href="classpcl_1_1_sample_consensus.html#a9f55f89ee72539f66f7edc8bcf6ce0c2">getModelCoefficients</a> (model_coefficients);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    model_coefficients[3] += std::numeric_limits&lt;float&gt;::epsilon ();    </div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    std::vector&lt;int&gt; support_indices;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    weight_sac.<a class="code" href="classpcl_1_1_sample_consensus.html#a7ac2013afb3a2feaaeb661f3aa3ccf6b">getInliers</a> (support_indices);</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160; </div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <span class="comment">// the support_indices which are actually cut (if not locally constrain:  cut_support_indices = support_indices</span></div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    std::vector&lt;int&gt; cut_support_indices;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keywordflow">if</span> (use_local_constrains_)</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    {</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;      Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      <span class="comment">// Cut the connections.</span></div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      <span class="comment">// We only interate through the points which are within the support (when we are local, otherwise all points in the segment).</span></div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      <span class="comment">// We also just acutally cut when the edge goes through the plane. This is why we check the planedistance</span></div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;      std::vector&lt;pcl::PointIndices&gt; cluster_indices;</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html">pcl::EuclideanClusterExtraction&lt;WeightSACPointType&gt;</a> euclidean_clusterer;</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;      pcl::search::KdTree&lt;WeightSACPointType&gt;::Ptr tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;WeightSACPointType&gt;</a>);</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;      tree-&gt;<a class="code" href="classpcl_1_1search_1_1_kd_tree.html#a457ebf1fc8f25e3b45d0bf9d55880f6f">setInputCloud</a> (edge_cloud_cluster);</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">setClusterTolerance</a> (seed_resolution_);</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">setMinClusterSize</a> (1);</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">setMaxClusterSize</a> (25000);</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">setSearchMethod</a> (tree);</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (edge_cloud_cluster);</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a> (boost::make_shared &lt;std::vector &lt;int&gt; &gt; (support_indices));</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a41e0cd5e3f7967d59013c967c909585c">extract</a> (cluster_indices);</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;<span class="comment">//       sv_adjacency_list_[seg_to_edgeID_map[itr-&gt;first][point_index]].used_for_cutting = true;</span></div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160; </div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> cc = 0; cc &lt; cluster_indices.size (); ++cc)</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      {</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;        <span class="comment">// get centroids of vertices        </span></div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        <span class="keywordtype">int</span> cluster_concave_pts = 0;</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="keywordtype">float</span> cluster_score = 0;</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Cluster has &quot; &lt;&lt; cluster_indices[cc].indices.size () &lt;&lt; &quot; points&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> cp = 0; cp &lt; cluster_indices[cc].indices.size (); ++cp)</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        {</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;          <span class="keywordtype">int</span> current_index = cluster_indices[cc].indices[cp];</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;          <span class="keywordtype">double</span> index_score;</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;          <span class="keywordflow">if</span> (use_directed_weights_)</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;            index_score = weights[current_index] * 1.414 * (fabsf (plane_normal.dot (edge_cloud_cluster-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (current_index).getNormalVector3fMap ())));</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          <span class="keywordflow">else</span></div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;            index_score = weights[current_index];</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;          cluster_score += index_score;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;          <span class="keywordflow">if</span> (weights[current_index] &gt; 0)</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;            ++cluster_concave_pts;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        <span class="comment">// check if the score is below the threshold. If that is the case this segment should not be split</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        cluster_score = cluster_score * 1.0 / cluster_indices[cc].indices.size ();</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Cluster score: &quot; &lt;&lt; cluster_score &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <span class="keywordflow">if</span> (cluster_score &gt;= min_cut_score_)</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;          cut_support_indices.insert (cut_support_indices.end (), cluster_indices[cc].indices.begin (), cluster_indices[cc].indices.end ());</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        }</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;      }</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;      <span class="keywordflow">if</span> (cut_support_indices.size () == 0)</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;      {</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Could not find planes which exceed required minumum score (threshold &quot; &lt;&lt; min_cut_score_ &lt;&lt; &quot;), not cutting&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      }</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    }</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    {</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      <span class="keywordtype">double</span> current_score = weight_sac.<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5a291239e9d29732e29919adc43f7ace">getBestScore</a> ();</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;      cut_support_indices = support_indices;</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;      <span class="comment">// check if the score is below the threshold. If that is the case this segment should not be split</span></div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;      <span class="keywordflow">if</span> (current_score &lt; min_cut_score_)</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;      {</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Score too low, no cutting&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      }</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    }</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160; </div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keywordtype">int</span> number_connections_cut = 0;</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> cs = 0; cs &lt; cut_support_indices.size (); ++cs)</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    {</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> point_index = cut_support_indices[cs];</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;      <span class="keywordflow">if</span> (use_clean_cutting_)</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      {</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="comment">// skip edges where both centroids are on one side of the cutting plane</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        uint32_t source_sv_label = sv_adjacency_list_[boost::source (seg_to_edgeIDs_map[itr-&gt;first][point_index], sv_adjacency_list_)];</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        uint32_t target_sv_label = sv_adjacency_list_[boost::target (seg_to_edgeIDs_map[itr-&gt;first][point_index], sv_adjacency_list_)];</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        <span class="comment">// get centroids of vertices</span></div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> source_centroid = sv_label_to_supervoxel_map_[source_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> target_centroid = sv_label_to_supervoxel_map_[target_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="comment">// this makes a clean cut</span></div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        <span class="keywordflow">if</span> (pcl::pointToPlaneDistanceSigned (source_centroid, model_coefficients) * pcl::pointToPlaneDistanceSigned (target_centroid, model_coefficients) &gt; 0)</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        {</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;        }</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;      }</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;      sv_adjacency_list_[seg_to_edgeIDs_map[itr-&gt;first][point_index]].used_for_cutting = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;      <span class="keywordflow">if</span> (sv_adjacency_list_[seg_to_edgeIDs_map[itr-&gt;first][point_index]].is_valid) </div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      {</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        ++number_connections_cut;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        sv_adjacency_list_[seg_to_edgeIDs_map[itr-&gt;first][point_index]].is_valid = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;      }</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    }</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;<span class="comment">//     std::cout &lt;&lt; &quot;We cut &quot; &lt;&lt; number_connections_cut &lt;&lt; &quot; connections&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    <span class="keywordflow">if</span> (number_connections_cut &gt; 0)</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      cut_found = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  }</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160; </div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  <span class="comment">// if not cut has been performed we can stop the recursion</span></div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  <span class="keywordflow">if</span> (cut_found)</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    doGrouping ();</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    --depth_levels_left;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    applyCuttingPlane (depth_levels_left);</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;  }</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    pcl::console::print_info (<span class="stringliteral">&quot;Could not find any more cuts, stopping recursion\n&quot;</span>);</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;}</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160; </div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;<span class="comment">/******************************************* Directional weighted RANSAC definitions ******************************************************************/</span>      </div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00290"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aaa2dc352bd71e275a23de67ac522974f">  290</a></span>&#160;<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aaa2dc352bd71e275a23de67ac522974f">pcl::CPCSegmentation&lt;PointT&gt;::WeightedRandomSampleConsensus::computeModel</a> (<span class="keywordtype">int</span>)</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;{</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;  <span class="comment">// Warn and exit if no threshold was set</span></div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a> == std::numeric_limits&lt;double&gt;::max ())</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;  {</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::CPCSegmentation&lt;PointT&gt;::WeightedRandomSampleConsensus::computeModel] No threshold set!\n&quot;</span>);</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;  }</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160; </div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;  <a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a> = 0;</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;  <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a> = -std::numeric_limits&lt;double&gt;::max ();</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160; </div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  std::vector&lt;int&gt; selection;</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;  Eigen::VectorXf model_coefficients;</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;  <span class="keywordtype">unsigned</span> skipped_count = 0;</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;  <span class="comment">// supress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!</span></div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> max_skip = <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a> * 10;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160; </div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;  <span class="comment">// Iterate</span></div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;  <span class="keywordflow">while</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a> &lt; <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a> &amp;&amp; skipped_count &lt; max_skip)</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;  {</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <span class="comment">// Get X samples which satisfy the model criteria and which have a weight &gt; 0</span></div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;setIndices (<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">model_pt_indices_</a>);</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;getSamples (<a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a>, selection);</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160; </div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    <span class="keywordflow">if</span> (selection.empty ())</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    {</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::CPCSegmentation&lt;PointT&gt;::WeightedRandomSampleConsensus::computeModel] No samples could be selected!\n&quot;</span>);</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    }</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160; </div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <span class="comment">// Search for inliers in the point cloud for the current plane model M</span></div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;computeModelCoefficients (selection, model_coefficients))</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    {</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;      <span class="comment">//++iterations_;</span></div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;      ++skipped_count;</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    }</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <span class="comment">// weight distances to get the score (only using connected inliers)</span></div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;setIndices (<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">full_cloud_pt_indices_</a>);</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160; </div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    boost::shared_ptr&lt;std::vector&lt;int&gt; &gt; current_inliers (<span class="keyword">new</span> std::vector&lt;int&gt;);</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;selectWithinDistance (model_coefficients, <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a>, *current_inliers);</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keywordtype">double</span> current_score = 0;</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; current_inliers-&gt;size (); ++i)</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    {</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;      <span class="keywordtype">int</span> current_index = current_inliers-&gt;at (i);</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;      <span class="keywordtype">double</span> index_score;</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">use_directed_weights_</a>)</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        <span class="comment">// the sqrt(2) factor was used in the paper and was meant for making the scores better comparable between directed and undirected weights</span></div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;        index_score = <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">weights_</a>[current_index] * 1.414 * (fabsf (plane_normal.dot (<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ab06480ee4efa1545f1fbf84ff58a5eca">point_cloud_ptr_</a>-&gt;at (current_index).getNormalVector3fMap ())));</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;      <span class="keywordflow">else</span></div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;        index_score = <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">weights_</a>[current_index];</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160; </div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      current_score += index_score;</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;    }</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;    <span class="comment">// normalize by the total number of inliers</span></div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    current_score = current_score * 1.0 / current_inliers-&gt;size ();</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    </div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    <span class="comment">// Better match ?</span></div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    <span class="keywordflow">if</span> (current_score &gt; <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a>)</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    {</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a> = current_score;</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;      <span class="comment">// Save the current model/inlier/coefficients selection as being the best so far</span></div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a> = selection;</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a> = model_coefficients;</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    }</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160; </div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;    ++<a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a>;</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    PCL_DEBUG (<span class="stringliteral">&quot;[pcl::CPCSegmentation&lt;PointT&gt;::WeightedRandomSampleConsensus::computeModel] Trial %d (max %d): score is %f (best is: %f so far).\n&quot;</span>, <a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a>, <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a>, current_score, <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a>);</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a> &gt; <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a>)</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    {</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;      PCL_DEBUG (<span class="stringliteral">&quot;[pcl::CPCSegmentation&lt;PointT&gt;::WeightedRandomSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n&quot;</span>);</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    }</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;  }</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;<span class="comment">//   std::cout &lt;&lt; &quot;Took us &quot; &lt;&lt; iterations_ - 1 &lt;&lt; &quot; iterations&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  PCL_DEBUG (<span class="stringliteral">&quot;[pcl::CPCSegmentation&lt;PointT&gt;::WeightedRandomSampleConsensus::computeModel] Model: %lu size, %f score.\n&quot;</span>, <a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a>.size (), <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a>);</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160; </div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a>.empty ())</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;  {</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a>.clear ();</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;  }</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160; </div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;  <span class="comment">// Get the set of inliers that correspond to the best model found so far</span></div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;  <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;selectWithinDistance (<a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a>, <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a>, <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a>);</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;}</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160; </div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_</span></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html">pcl::CPCSegmentation::WeightedRandomSampleConsensus</a></div><div class="ttdoc">WeightedRandomSampleConsensus represents an implementation of the Directionally Weighted RANSAC algor...</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:177</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a5a291239e9d29732e29919adc43f7ace"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5a291239e9d29732e29919adc43f7ace">pcl::CPCSegmentation::WeightedRandomSampleConsensus::getBestScore</a></div><div class="ttdeci">double getBestScore() const</div><div class="ttdoc">Get the best score</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:240</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a5f272fa6787532bbe7a7f14ef40f79d6"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">pcl::CPCSegmentation::WeightedRandomSampleConsensus::weights_</a></div><div class="ttdeci">std::vector&lt; double &gt; weights_</div><div class="ttdoc">vector of weights assigned to points. Set by the setWeights-method</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:262</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a6313e39510545b961cfbed0373b7bbde"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a6313e39510545b961cfbed0373b7bbde">pcl::CPCSegmentation::WeightedRandomSampleConsensus::setWeights</a></div><div class="ttdeci">void setWeights(const std::vector&lt; double &gt; &amp;weights, const bool directed_weights=false)</div><div class="ttdoc">Set the weights for the input points</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:218</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a690b982ca4e9efbc8cc8bfd1954db4dc"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">pcl::CPCSegmentation::WeightedRandomSampleConsensus::model_pt_indices_</a></div><div class="ttdeci">boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; model_pt_indices_</div><div class="ttdoc">The indices used for estimating the RANSAC model. Only those whose weight is &gt; 0</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:265</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_aaa2dc352bd71e275a23de67ac522974f"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aaa2dc352bd71e275a23de67ac522974f">pcl::CPCSegmentation::WeightedRandomSampleConsensus::computeModel</a></div><div class="ttdeci">bool computeModel(int debug_verbosity_level=0)</div><div class="ttdoc">Compute the actual model and find the inliers</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:290</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_ab06480ee4efa1545f1fbf84ff58a5eca"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ab06480ee4efa1545f1fbf84ff58a5eca">pcl::CPCSegmentation::WeightedRandomSampleConsensus::point_cloud_ptr_</a></div><div class="ttdeci">boost::shared_ptr&lt; const pcl::PointCloud&lt; WeightSACPointType &gt; &gt; point_cloud_ptr_</div><div class="ttdoc">Pointer to the input PointCloud</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:271</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_ac8f5af30b240aa1d7c21082ef2f84ed7"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">pcl::CPCSegmentation::WeightedRandomSampleConsensus::full_cloud_pt_indices_</a></div><div class="ttdeci">boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; full_cloud_pt_indices_</div><div class="ttdoc">The complete list of indices used for the model evaluation</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:268</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_afdf74f8e57d514108d59d16829b5b446"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">pcl::CPCSegmentation::WeightedRandomSampleConsensus::use_directed_weights_</a></div><div class="ttdeci">bool use_directed_weights_</div><div class="ttdoc">weight each positive weight point by the inner product between the normal and the plane normal</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:259</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_aff0d5c9c04a5d9dee5b66f44c04303ec"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">pcl::CPCSegmentation::WeightedRandomSampleConsensus::best_score_</a></div><div class="ttdeci">double best_score_</div><div class="ttdoc">Highest score found so far</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:274</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html">pcl::CPCSegmentation</a></div><div class="ttdoc">A segmentation algorithm partitioning a supervoxel graph. It uses planar cuts induced by local concav...</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:69</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a0ff7ee11473d36cbb774f90de8064908"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">pcl::CPCSegmentation::segment</a></div><div class="ttdeci">void segment()</div><div class="ttdoc">Merge supervoxels using cuts through local convexities. The input parameters are generated by using t...</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:60</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aba7a4f7d9481b0c9c88edc6d301964d9"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">pcl::CPCSegmentation::applyCuttingPlane</a></div><div class="ttdeci">void applyCuttingPlane(uint32_t depth_levels_left)</div><div class="ttdoc">Used in for CPC to find and fit cutting planes to the pointcloud.</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:86</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html">pcl::EuclideanClusterExtraction</a></div><div class="ttdoc">EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sen...</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:296</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a096af3508dd19b23a726a8323f7c7bba"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">pcl::EuclideanClusterExtraction::setMinClusterSize</a></div><div class="ttdeci">void setMinClusterSize(int min_cluster_size)</div><div class="ttdoc">Set the minimum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:356</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a41e0cd5e3f7967d59013c967c909585c"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a41e0cd5e3f7967d59013c967c909585c">pcl::EuclideanClusterExtraction::extract</a></div><div class="ttdeci">void extract(std::vector&lt; PointIndices &gt; &amp;clusters)</div><div class="ttdoc">Cluster extraction in a PointCloud given by &lt;setInputCloud (), setIndices ()&gt;</div><div class="ttdef"><b>Definition:</b> extract_clusters.hpp:210</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a8fb42fea2e8bfca4ebadf4339335cf11"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">pcl::EuclideanClusterExtraction::setClusterTolerance</a></div><div class="ttdeci">void setClusterTolerance(double tolerance)</div><div class="ttdoc">Set the spatial cluster tolerance as a measure in the L2 Euclidean space</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:340</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_ac4162a11c1fd5797d507068a056bfbf7"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">pcl::EuclideanClusterExtraction::setSearchMethod</a></div><div class="ttdeci">void setSearchMethod(const KdTreePtr &amp;tree)</div><div class="ttdoc">Provide a pointer to the search object.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:322</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_adb0be906f101b309506cdc37ffd31624"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">pcl::EuclideanClusterExtraction::setMaxClusterSize</a></div><div class="ttdeci">void setMaxClusterSize(int max_cluster_size)</div><div class="ttdoc">Set the maximum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:372</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_ab219359de6eb34c9d51e2e976dd1a0d1"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">pcl::PCLBase::setIndices</a></div><div class="ttdeci">virtual void setIndices(const IndicesPtr &amp;indices)</div><div class="ttdoc">Provide a pointer to the vector of indices that represents the input data.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:73</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a1155fe4ba5cdc7e83cb72159a4ea02dc"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">pcl::PointCloud::at</a></div><div class="ttdeci">const PointT &amp; at(int column, int row) const</div><div class="ttdoc">Obtain the point given by the (column, row) coordinates. Only works on organized datasets (those that...</div><div class="ttdef"><b>Definition:</b> point_cloud.h:283</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a0115926eadf78f7bc1ad4675659d8343"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">pcl::SampleConsensus&lt; WeightSACPointType &gt;::inliers_</a></div><div class="ttdeci">std::vector&lt; int &gt; inliers_</div><div class="ttdoc">The indices of the points that were chosen as inliers after the last computeModel () call.</div><div class="ttdef"><b>Definition:</b> sac.h:316</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a0e04da16522ae180cb8cc2e6ef0d2244"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">pcl::SampleConsensus&lt; WeightSACPointType &gt;::model_</a></div><div class="ttdeci">std::vector&lt; int &gt; model_</div><div class="ttdoc">The model found after the last computeModel () as point cloud indices.</div><div class="ttdef"><b>Definition:</b> sac.h:313</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a471e062f42e9cb4ae9d77107cc135acb"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">pcl::SampleConsensus&lt; WeightSACPointType &gt;::iterations_</a></div><div class="ttdeci">int iterations_</div><div class="ttdoc">Total number of internal loop iterations that we've done so far.</div><div class="ttdef"><b>Definition:</b> sac.h:325</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a7ac2013afb3a2feaaeb661f3aa3ccf6b"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a7ac2013afb3a2feaaeb661f3aa3ccf6b">pcl::SampleConsensus::getInliers</a></div><div class="ttdeci">void getInliers(std::vector&lt; int &gt; &amp;inliers)</div><div class="ttdoc">Return the best set of inliers found so far for this model.</div><div class="ttdef"><b>Definition:</b> sac.h:300</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a96f852dfca500689684313d3cb7f84b1"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">pcl::SampleConsensus&lt; WeightSACPointType &gt;::model_coefficients_</a></div><div class="ttdeci">Eigen::VectorXf model_coefficients_</div><div class="ttdoc">The coefficients of our model computed directly from the model found.</div><div class="ttdef"><b>Definition:</b> sac.h:319</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a9f55f89ee72539f66f7edc8bcf6ce0c2"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a9f55f89ee72539f66f7edc8bcf6ce0c2">pcl::SampleConsensus::getModelCoefficients</a></div><div class="ttdeci">void getModelCoefficients(Eigen::VectorXf &amp;model_coefficients)</div><div class="ttdoc">Return the model coefficients of the best model found so far.</div><div class="ttdef"><b>Definition:</b> sac.h:306</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa1c52d7d8be8f058feac1f9241bf305e"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">pcl::SampleConsensus&lt; WeightSACPointType &gt;::threshold_</a></div><div class="ttdeci">double threshold_</div><div class="ttdoc">Distance to model threshold.</div><div class="ttdef"><b>Definition:</b> sac.h:328</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa4953d080c1ab4223cde8ff8d8cabc52"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">pcl::SampleConsensus&lt; WeightSACPointType &gt;::sac_model_</a></div><div class="ttdeci">SampleConsensusModelPtr sac_model_</div><div class="ttdoc">The underlying data model used (i.e. what is it that we attempt to search for).</div><div class="ttdef"><b>Definition:</b> sac.h:310</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_ab5ca8dbf21b2a1c6ed9c1e8d3eba853c"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">pcl::SampleConsensus&lt; WeightSACPointType &gt;::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">Maximum number of iterations before giving up.</div><div class="ttdef"><b>Definition:</b> sac.h:331</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_af8558bc2462b6da4a2f88b2efc1ad571"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#af8558bc2462b6da4a2f88b2efc1ad571">pcl::SampleConsensus::setMaxIterations</a></div><div class="ttdeci">void setMaxIterations(int max_iterations)</div><div class="ttdoc">Set the maximum number of iterations.</div><div class="ttdef"><b>Definition:</b> sac.h:150</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_plane_html"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model_plane.html">pcl::SampleConsensusModelPlane</a></div><div class="ttdoc">SampleConsensusModelPlane defines a model for 3D plane segmentation. The model coefficients are defin...</div><div class="ttdef"><b>Definition:</b> sac_model_plane.h:137</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree</a></div><div class="ttdoc">search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...</div><div class="ttdef"><b>Definition:</b> kdtree.h:63</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html_a457ebf1fc8f25e3b45d0bf9d55880f6f"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html#a457ebf1fc8f25e3b45d0bf9d55880f6f">pcl::search::KdTree::setInputCloud</a></div><div class="ttdeci">void setInputCloud(const PointCloudConstPtr &amp;cloud, const IndicesConstPtr &amp;indices=IndicesConstPtr())</div><div class="ttdoc">Provide a pointer to the input dataset.</div><div class="ttdef"><b>Definition:</b> kdtree.hpp:77</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_i_normal_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_i_normal.html">pcl::PointXYZINormal</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates...</div><div class="ttdef"><b>Definition:</b> point_types.hpp:969</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_a_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, and the RGBA color.</div><div class="ttdef"><b>Definition:</b> point_types.hpp:540</div></div>
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